| Causal intervention for leveraging popularity bias in recommendation Y Zhang, F Feng, X He, T Wei, C Song, G Ling, Y Zhang Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 310 | 2021 |
| Tallrec: An effective and efficient tuning framework to align large language model with recommendation K Bao, J Zhang, Y Zhang, W Wang, F Feng, X He Proceedings of the 17th ACM Conference on Recommender Systems, 1007-1014, 2023 | 128 | 2023 |
| How to retrain recommender system? A sequential meta-learning method Y Zhang, F Feng, C Wang, X He, M Wang, Y Li, Y Zhang Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 108 | 2020 |
| Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation J Zhang, K Bao, Y Zhang, W Wang, F Feng, X He Proceedings of the 17th ACM Conference on Recommender Systems, 993-999, 2023 | 69 | 2023 |
| A bi-step grounding paradigm for large language models in recommendation systems K Bao, J Zhang, W Wang, Y Zhang, Z Yang, Y Luo, F Feng, X He, Q Tian arXiv preprint arXiv:2308.08434, 2023 | 33 | 2023 |
| Addressing confounding feature issue for causal recommendation X He, Y Zhang, F Feng, C Song, L Yi, G Ling, Y Zhang ACM Transactions on Information Systems 41 (3), 1-23, 2023 | 25 | 2023 |
| Collm: Integrating collaborative embeddings into large language models for recommendation Y Zhang, F Feng, J Zhang, K Bao, Q Wang, X He arXiv preprint arXiv:2310.19488, 2023 | 20 | 2023 |
| Mitigating hidden confounding effects for causal recommendation X Zhu, Y Zhang, X Yang, D Wang, F Feng IEEE Transactions on Knowledge and Data Engineering, 2024 | 14 | 2024 |
| Causal recommendation: Progresses and future directions W Wang, Y Zhang, H Li, P Wu, F Feng, X He Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 12 | 2023 |
| Recommendation unlearning via influence function Y Zhang, Z Hu, Y Bai, F Feng, J Wu, Q Wang, X He arXiv preprint arXiv:2307.02147, 2023 | 8 | 2023 |
| Rethinking missing data: Aleatoric uncertainty-aware recommendation C Wang, F Feng, Y Zhang, Q Wang, X Hu, X He IEEE Transactions on Big Data, 2023 | 6 | 2023 |
| Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation Y Zhang, T Shi, F Feng, W Wang, D Wang, X He, Y Zhang Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 6 | 2023 |
| Large language models for recommendation: Progresses and future directions K Bao, J Zhang, Y Zhang, W Wenjie, F Feng, X He Proceedings of the Annual International ACM SIGIR Conference on Research and …, 2023 | 5 | 2023 |
| Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework Y Zhang, Y Bai, J Chang, X Zang, S Lu, J Lu, F Feng, Y Niu, Y Song Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 5 | 2023 |
| Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems T Shi, Y Zhang, Z Xu, C Chen, F Feng, X He, Q Tian arXiv preprint arXiv:2312.15599, 2023 | 4 | 2023 |
| Accepted tutorials at the web conference 2022 R Tommasini, S Basu Roy, X Wang, H Wang, H Ji, J Han, P Nakov, ... Companion Proceedings of the Web Conference 2022, 391-399, 2022 | 2 | 2022 |
| Prospect Personalized Recommendation on Large Language Model-based Agent Platform J Zhang, K Bao, W Wang, Y Zhang, W Shi, W Xu, F Feng, TS Chua arXiv preprint arXiv:2402.18240, 2024 | 1 | 2024 |
| Towards Trustworthy Recommender System: A Faithful and Responsible Recommendation Perspective Y Zhang Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 1 | 2023 |
| Mitigating spurious correlations for self-supervised recommendation XY Lin, YY Xu, WJ Wang, Y Zhang, FL Feng Machine Intelligence Research 20 (2), 263-275, 2023 | 1 | 2023 |
| Exact and Efficient Unlearning for Large Language Model-based Recommendation Z Hu, Y Zhang, M Xiao, W Wang, F Feng, X He arXiv preprint arXiv:2404.10327, 2024 | | 2024 |
